Benchmarking Pretrained Attention-based Models for Real-Time Recognition in Robot-Assisted Esophagectomy
Eindhoven University of Technology（埃因霍温理工大学）；University Medical Center Utrecht（乌得勒支大学医学中心）
Ronald L. P. D. de Jong, Yasmina al Khalil, Tim J. M. Jaspers, Romy C. van Jaarsveld, Gino M. Kuiper, Yiping Li, Richard van Hillegersberg, Jelle P. Ruurda, Mar… 展开作者
Ronald L. P. D. de Jong, Yasmina al Khalil, Tim J. M. Jaspers, Romy C. van Jaarsveld, Gino M. Kuiper, Yiping Li, Richard van Hillegersberg, Jelle P. Ruurda, Marcel Breeuwer, Fons van der Sommen
备注：
Accepted for presentation at the SPIE Medical Imaging Conference, 2025
Esophageal cancer is among the most common types of cancer worldwide. It is traditionally treated using open esophagectomy, but in recent years, robot-assisted minimally invasive esophagectomy (RAMIE) has emerged as a promising alternative. However, robot-assisted surgery can be challenging for novice surgeons, as they often suffer from a loss of spatial orientation. Computer-aided anatomy recognition holds promise for improving surgical navigation, but research in this area remains limited. In this study, we developed a comprehensive dataset for semantic segmentation in RAMIE, featuring the largest collection of vital anatomical structures and surgical instruments to date. Handling this diverse set of classes presents challenges, including class imbalance and the recognition of complex structures such as nerves. This study aims to understand the challenges and limitations of current state-of-the-art algorithms on this novel dataset and problem. Therefore, we benchmarked eight real-time deep learning models using two pretraining datasets. We assessed both traditional and attention-based networks, hypothesizing that attention-based networks better capture global patterns and address challenges such as occlusion caused by blood or other tissues. The benchmark includes our RAMIE dataset and the publicly available CholecSeg8k dataset, enabling a thorough assessment of surgical segmentation tasks. Our findings indicate that pretraining on ADE20k, a dataset for semantic segmentation, is more effective than pretraining on ImageNet. Furthermore, attention-based models outperform traditional convolutional neural networks, with SegNeXt and Mask2Former achieving higher Dice scores, and Mask2Former additionally excelling in average symmetric surface distance.
Benchmarking Domain Generalization Algorithms in Computational Pathology
Tissue Image Analytics centre, Department of Computer Science（组织机构：组织机构，计算机科学系）；Histofy Ltd（Histofy有限公司）；ICMR-National Institute of Pathology（ICMR国家病理研究所）
Benchmarking Foundation Models and Parameter-Efficient Fine-Tuning for Prognosis Prediction in Medical Imaging
Unit of Artificial Intelligence and Computer Systems, Department of Engineering, Università Campus Bio-Medico di Roma（人工智能与计算机系统单位，工程系，罗马生物医学大学）；College of Computer Science and Software Engineering, Shenzhen University（计算机科学与软件工程学院，深圳大学）；Department of Diagnostics and Intervention, Radiation Physics, Biomedical Engineering, Umeå University（诊断与介入系，辐射物理，生物医学工程，乌梅大学）
Beyond Natural-Image Foundation Models: Benchmarking Satellite Pretraining for Ophthalmic Image Analysis
Faculty of Electrical Engineering and Computing, University of Zagreb（萨格勒布大学电气工程与计算机学院）；University College London（伦敦大学学院）；Institute of Ophthalmology, University College London（伦敦大学学院眼科研究所）；Department of Computer Science, University College London（伦敦大学学院计算机科学系）；NIHR Moorfields Biomedical Research Centre（NIHR穆尔菲尔德生物医学研究中心）；Hawkes Institute, University College London（伦敦大学学院霍克斯研究所）；Moorfields Eye Hospital NHS Foundation Trust（穆尔菲尔德眼科医院NHS基金会信托）
Benchmarking Ophthalmology Foundation Models for Clinically Significant Age Macular Degeneration Detection
Faculty of Biomedical Engineering, Technion-IIT（技术学院生物医学工程系）；Taub Faculty of Computer Science, Technion-IIT（技术学院计算机科学系）；Faculty of Mathematics, Technion-IIT（技术学院数学系）；Department of Ophthalmology, Hillel Yaffe Medical Center（希勒尔·亚法医疗中心眼科部）；The Ruth and Rappaport Faculty of Medicine, Technion-IIT（技术学院医学院）；Ophthalmology Department, São Paulo Federal University（圣保罗联邦大学眼科部）；Medical Engineering and Science, Massachusetts Institute of Technology（麻省理工学院医学工程与科学系）；The Adelson School of Medicine, Ariel University（阿里尔大学阿德勒医学学校）
WiSE-OD: Benchmarking Robustness in Infrared Object Detection
Laboratoire d’imagerie, de vision et d’intelligence artificielle (LIVIA)（图像、视觉与人工智能实验室）；International Laboratory on Learning Systems (ILLS)（学习系统国际实验室）；Dept. of Systems Engineering, ETS Montreal, Canada（系统工程系，蒙特利尔大学）
BioVFM-21M: Benchmarking and Scaling Self-Supervised Vision Foundation Models for Biomedical Image Analysis
Paul C. Lauterbur Research Center for Biomedical Imaging（生物医学成像保罗·劳特伯研究中心）；Shenzhen Institutes of Advanced Technology（深圳先进技术研究院）；Chinese Academy of Sciences（中国科学院）；Pengcheng Laboratory（鹏城实验室）；University of Chinese Academy of Sciences（中国科学院大学）；Faculty of Applied Sciences（应用科学学院）
MMR-AD: A Large-Scale Multimodal Dataset for Benchmarking General Anomaly Detection with Multimodal Large Language Models
School of Information Science and Electronic Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）；MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University（上海交通大学人工智能研究院教育部人工智能重点实验室）
LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning
University of Wisconsin, Madison（威斯康星大学麦迪逊分校）；University of Washington（华盛顿大学）；Georgia Institute of Technology（佐治亚理工学院）；University of California, Riverside（加州大学河滨分校）
Benchmarking Deep Learning Models for Laryngeal Cancer Staging Using the LaryngealCT Dataset
School of Information Technology, Deakin University（德肯大学信息科技学院）；Department of Head and Neck Surgery, Kasturba Medical College, Manipal Academy of Higher Education（曼谷医学院头颈外科部门，曼谷高等教育学院）；Department of Radiodiagnosis and Imaging, Kasturba Medical College, Manipal Academy of Higher Education（曼谷医学院放射诊断与影像部门，曼谷高等教育学院）；Manipal Institute of Technology, Manipal Academy of Higher Education（曼谷技术学院，曼谷高等教育学院）
Reproducible Benchmarking for Lung Nodule Detection and Malignancy Classification Across Multiple Low-Dose CT Datasets
Duke University School of Medicine（杜克大学医学院）；Pratt School of Engineering, Duke University（杜克大学普拉特工程学院）；University of Colorado（科罗拉多大学）；Duke University（杜克大学）
Radiomics--Foundation Fusion for Interpretable RCC Classification: Internal Benchmarking and Exploratory External Transfer
Research Ireland Centre for Research Training in Machine Learning（爱尔兰研究机构机器学习研究培训中心）；University College Dublin（都柏林大学学院）；School of Veterinary Medicine, University College Dublin（都柏林大学学院兽医学院）
MulTaBench: Benchmarking Multimodal Tabular Learning with Text and Image
Technion – Israel Institute of Technology（技术ion – 以色列理工学院）；Prior Labs（Prior实验室）；NVIDIA；SODA Team, INRIA Saclay, Palaiseau（SODA团队，INRIA萨克莱，帕莱索）；University of Freiburg（弗赖堡大学）；Probabl；ELLIS Institute Tübingen（图宾根ELLIS研究所）
Neural network pruning typically removes connections or neurons from a pretrained converged model; while a new pruning paradigm, pruning at initialization (PaI), attempts to prune a randomly initialized network. This paper offers the first survey concentrated on this emerging pruning fashion. We first introduce a generic formulation of neural network pruning, followed by the major classic pruning topics. Then, as the main body of this paper, a thorough and structured literature review of PaI methods is presented, consisting of two major tracks (sparse training and sparse selection). Finally, w…
Benchmarking Pathology Foundation Models for Breast Cancer Survival Prediction
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet（卡罗林斯卡研究所医学流行病学与生物统计学系）；Department of Engineering Science, University of Oxford（牛津大学工程科学系）；Division of Oncology, Department of Clinical Sciences Lund, Lund University（卢德大学临床科学系肿瘤学系）；Oxford Suzhou Centre for Advanced Research, University of Oxford（牛津大学苏州先进研究中心）
Trained on large datasets, deep learning (DL) can accurately classify videos into hundreds of diverse classes. However, video data is expensive to annotate. Zero-shot learning (ZSL) proposes one solution to this problem. ZSL trains a model once, and generalizes to new tasks whose classes are not present in the training dataset. We propose the first end-to-end algorithm for ZSL in video classification. Our training procedure builds on insights from recent video classification literature and uses a trainable 3D CNN to learn the visual features. This is in contrast to previous video ZSL methods, …
GEO-Bench-2: From Performance to Capability, Rethinking Evaluation in Geospatial AI
IBM Research Europe（IBM欧洲研究院）；Technical University Munich（慕尼黑技术大学）；Clark University（克拉克大学）；MBZUAI；NASA Impact（NASA影响计划）；ESA Φ \Phi -lab（欧洲航天局Φ实验室）；Arizona State University（亚利桑那州立大学）；ServiceNow AI Research（ServiceNow人工智能研究）
A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning
Department of Computer Science, University of Copenhagen（计算机科学系，哥本哈根大学）；Pioneer Centre For AI（人工智能先锋中心）；Copenhagen Research Centre for Biological and Precision Psychiatry（哥本哈根生物与精准精神病学研究中心）；Mental Health Centre Copenhagen（哥本哈根心理健康中心）；Copenhagen University Hospital（哥本哈根大学医院）；Rigshospitalet（皇家医院）；Radiological AI Testcenter（放射学AI测试中心）；Athinoula A. Martinos Center for Biomedical Imaging（Athinoula A. Martinos生物医学成像中心）；Massachusetts General Hospital and Harvard Medical School（麻省总医院和哈佛医学院）；Johns Hopkins University（约翰霍普金斯大学）
Cisco Systems（思科系统）；University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）；BanglaLLM；Dalhousie University（达尔豪斯大学）；University of Central Florida（中佛罗里达大学）
GlobalGeoTree: A Multi-Granular Vision-Language Dataset for Global Tree Species Classification
Technical University of Munich（慕尼黑技术大学）；University of Reading（reading大学）；University of Vienna（维也纳大学）；University of Göttingen（哥廷根大学）；University of Konstanz（康斯坦茨大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）
STSBench: A Large-Scale Dataset for Modeling Neuronal Activity in the Dorsal Stream of Primate Visual Cortex
Neuroscience Interdepartmental Program, Stanford University（斯坦福大学神经科学跨学科项目）；Department of Neurobiology, Stanford University（斯坦福大学神经生物学系）；Howard Hughes Medical Institute, Stanford University（斯坦福大学霍华德·霍夫曼医学研究所）
Omni-iEEG: A Large-Scale, Comprehensive iEEG Dataset and Benchmark for Epilepsy Research
UCLA Samueli School of Engineering（UCLA 工程学院）；UCLA Mattel Children’s Hospital, David Geffen School of Medicine（UCLA 马特尔儿童医院，大卫·格芬医学院）；Children’s Hospital of Michigan, Wayne State University School of Medicine（密歇根儿童医院，韦恩州立大学医学院）
Replication Study and Benchmarking of Real-Time Object Detection Models
Département de physique, de génie physique et d’optique, Université Laval（物理、物理工程与光学系，拉瓦尔大学）；Département de génie électrique et de génie informatique, Université Laval（电气工程与计算机工程系，拉瓦尔大学）；Département d’informatique et de génie logiciel, Université Laval（计算机科学与软件工程系，拉瓦尔大学）
Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge
organization= Department of Computer Science, University of Copenhagen , city= Copenhagen , country= Denmark；organization= Pioneer Centre for AI , city= Copenhagen , country= Denmark；organization= Copenhagen Research Centre for Biological；Precision Psychiatry, Mental Health Centre Copenhagen, Copenhagen University Hospital , region= Capital Region of Denmark , city= Copenhagen , country= Denmark；organization= Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital；Harvard Medical School , city= Boston , state= Massachusetts , country= USA；Artificial Intelligence Laboratory, Massachusetts Institute of Technology , city= Boston , state= Massachusetts , country= USA；organization= Johns Hopkins University , city= Baltimore , state= Maryland , country= USA；organization= Radiological AI Testcenter (RAIT) , region= Capital Region of Denmark , city= Copenhagen , country= Denmark；organization= Copenhagen University Hospital, Rigshospitalet , region= Capital Region of Denmark , city= Copenhagen , country= Denmark；organization= Copenhagen University Hospital, Bispebjerg \& Frederiksberg Hospital , region= Capital Region of Denmark , city= Copenhagen , country= Denmark；organization= Department of Clinical Medicine, Faculty of Health；Medical Sciences, University of Copenhagen , city= Copenhagen , country= Denmark；organization= Division of Medical Image Computing, German Cancer Research Center (DKFZ) , city= Heidelberg , country= Germany；organization= University of British Columbia , city= Vancouver , state= British Columbia , country= Canada；organization= Hawkes Institute, Department of Computer Science, University College London , city= London , country= United Kingdom；Lung Institute, Faculty of Medicine, Imperial College London , city= London , country= United Kingdom；organization= Department of Applied Mathematics, Technical Medical Centre, University of Twente , city= Enschede , country= Netherlands；organization= IISLAB, Technical University of Košice , city= Košice , country= Slovakia；organization= 2nd Department of Internal Medicine, Pavol Jozef Safarik University；L Pasteur University Hospital , city= Košice , country= Slovakia；organization= Fudan University , city= Shanghai , country= China；organization= Shenzhen Technology University , city= Shenzhen , country= China；organization= Department of Radiology, Lausanne University Hospital；University of Lausanne , city= Lausanne , country= Switzerland；organization= Louvain Neuroinflammation Imaging Lab (NIL), Université Catholique de Louvain , city= Brussels , country= Belgium；organization= University of Applied Sciences；organization= CIBM Center for Biomedical Imaging , city= Lausanne , country= Switzerland；organization= Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology；Reproduction, Maastricht University Medical Centre+ , city= Maastricht , country= The Netherlands；organization= Department of Biomedical Engineering, Medical Image Analysis, Eindhoven University of Technology , city= Eindhoven , country= The Netherlands；organization= Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences , city= Shenzhen , country= China；organization= McGill University；Mila - Quebec AI Institute , city= Montreal , country= Canada；organization= Hotchkiss Brain Institute；Department of Radiology, University of Calgary , city= Calgary , state= Alberta , country= Canada；organization= Department of Radiology, University of Calgary , city= Calgary , state= Alberta , country= Canada；organization= Alberta Children's Hospital Research Institute, Department of Clinical Neuroscience, University of Calgary , city= Calgary , state= Alberta , country= Canada；organization= The Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech；Emory University , city= Atlanta , state= Georgia , country= USA；organization= SGGS College of Engineering；organization= Seoul National University , city= Seoul , country= South Korea；organization= The D-Lab, Department of Precision Medicine, GROW Research Institute for Oncology；Reproduction, Maastricht University , city= Maastricht , country= The Netherlands；organization= Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School , city= Boston , state= Massachusetts , country= USA；Nuclear Medicine, CARIM \& GROW, Maastricht University , city= Maastricht , country= The Netherlands；organization= Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham；Women’s Hospital, Harvard Medical School , city= Boston , state= Massachusetts , country= USA；Learning Group, Heidelberg University Hospital , city= Heidelberg , country= Germany
Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks
New York University（纽约大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；University of Maryland（马里兰大学）；Georgia Institute of Technology（佐治亚理工学院）；Inria（法国国家信息与自动化研究所）；Meta AI Research（元人工智能研究部门）
本文提出Battle of the Backbones (BoB)基准，跨多种计算机视觉任务比较1500+次训练运行的预训练模型，发现监督预训练CNN在多数任务表现最佳，而SSL骨干网络在公平比较中极具竞争力。
MedNeXt-v2: Scaling 3D ConvNeXts for Large-Scale Supervised Representation Learning in Medical Image Segmentation
German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing（德国癌症研究中心（DKFZ）海德堡，医学影像计算部）；Faculty of Mathematics and Computer Science, Heidelberg University（海德堡大学数学与计算机科学学院）；HIDSS4Health - Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Germany（HIDSS4Health - 欧洲信息与数据科学健康学校，卡尔斯鲁厄/海德堡，德国）；Medical Faculty Heidelberg, Heidelberg University（海德堡大学医学学院）；National Center for Tumor Diseases (NCT), Heidelberg, Germany（肿瘤国家研究中心（NCT），海德堡，德国）；Helmholtz Imaging, German Cancer Research Center, Germany（海德堡成像，德国癌症研究中心，德国）；Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital（模式分析与学习组，放射肿瘤学部，海德堡大学医院）
